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MNIST Dataset (MNIST_dataset)

Overview

This DeepTrackAI repository provides a copy of the MNIST dataset, a benchmark collection of handwritten digits originally created by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges, originally available from the Official MNIST Website.

Each image is a grayscale depiction of a digit from 0 to 9, with an associated label. MNIST is one of the most widely used datasets for training and evaluating machine learning and deep learning models in computer vision.

Summary

  • Number of images: 70,000 (60,000 training, 10,000 test)
  • Image size: 28 × 28 pixels
  • Image format: 8-bit grayscale PNG
  • Labels: 10 classes (digits 0–9)

Original Source

If you use this dataset in your research, please follow the licensing requirements and properly attribute the original authors.


Dataset Structure

/MNIST_dataset
└── mnist/
    ├── train/          # Training images (labeled by filename)
    │   ├── 0_000000.png
    │   ├── 0_000001.png
    │   └── ...
    └── test/           # Test images (labeled by filename)
        ├── 0_000000.png
        ├── 0_000001.png
        └── ...

Each filename begins with its class label (0–9), followed by a sequential numerical identifier.


How to Access the Data

Clone the Repository

git clone github.com/DeepTrackAI/MNIST_dataset
cd MNIST_dataset

Attribution

If you use this dataset, please cite both the MNIST dataset and the reference article.

Cite the dataset:

LeCun Y, Cortes C, Burges CJC. The MNIST Database of Handwritten Digits. Retrieved from yann.lecun.com/exdb/mnist/

@misc{lecun1998mnist,
  title        = {The MNIST Database of Handwritten Digits},
  author       = {LeCun, Yann and Cortes, Corinna and Burges, Christopher J.C.},
  year         = {1998},
  howpublished = {\url{http://yann.lecun.com/exdb/mnist/}}
}

Cite the reference article:

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278–2324 (1998). DOI: 10.1109/5.726791

@article{lecun1998gradient,
  title     = {Gradient-based learning applied to document recognition},
  author    = {LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal   = {Proceedings of the IEEE},
  volume    = {86},
  number    = {11},
  pages     = {2278--2324},
  year      = {1998},
  publisher = {IEEE},
  doi       = {10.1109/5.726791}
}

License

This replication dataset is shared under the Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) License, consistent with the original licensing terms.

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